diff --git a/src/transformers/models/perceiver/modeling_perceiver.py b/src/transformers/models/perceiver/modeling_perceiver.py
index 7008b04ec82f..9d2e2fcf9882 100755
--- a/src/transformers/models/perceiver/modeling_perceiver.py
+++ b/src/transformers/models/perceiver/modeling_perceiver.py
@@ -2201,7 +2201,7 @@ def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, su
                 pos_emb = self.output_position_encodings(batch_size)
             elif self.position_encoding_type == "fourier":
                 pos_emb = self.output_position_encodings(
-                    self.output_index_dims, batch_size=batch_size, device=inputs.device, pos=pos
+                    self.output_index_dims, batch_size=batch_size, device=inputs.device, dtype=inputs.dtype, pos=pos
                 )
 
             # Optionally project them to a target dimension.
@@ -2215,7 +2215,9 @@ def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, su
             if self.position_encoding_type == "trainable":
                 pos_emb = self.output_position_encodings(batch_size)
             elif self.position_encoding_type == "fourier":
-                pos_emb = self.output_position_encodings(index_dims, batch_size, device=inputs.device)
+                pos_emb = self.output_position_encodings(
+                    index_dims, batch_size, device=inputs.device, dtype=inputs.dtype
+                )
 
             # Optionally project them to a target dimension.
             pos_emb = self.positions_projection(pos_emb)
@@ -2816,7 +2818,12 @@ def output_size(self):
         return encoding_size
 
     def forward(
-        self, index_dims: List[int], batch_size: int, device, pos: torch.FloatTensor = None
+        self,
+        index_dims: List[int],
+        batch_size: int,
+        device: torch.device,
+        dtype: torch.dtype,
+        pos: torch.FloatTensor = None,
     ) -> torch.FloatTensor:
         pos = _check_or_build_spatial_positions(pos, index_dims, batch_size)
         fourier_pos_enc = generate_fourier_features(
@@ -2825,7 +2832,7 @@ def forward(
             max_resolution=self.max_resolution,
             concat_pos=self.concat_pos,
             sine_only=self.sine_only,
-        ).to(device)
+        ).to(device=device, dtype=dtype)
         return fourier_pos_enc
 
 
@@ -3156,7 +3163,7 @@ def _build_network_inputs(self, inputs: torch.Tensor, network_input_is_1d: bool
         if self.position_encoding_type == "trainable":
             pos_enc = self.position_embeddings(batch_size)
         elif self.position_encoding_type == "fourier":
-            pos_enc = self.position_embeddings(index_dims, batch_size, device=inputs.device)
+            pos_enc = self.position_embeddings(index_dims, batch_size, device=inputs.device, dtype=inputs.dtype)
 
         # Optionally project them to a target dimension.
         pos_enc = self.positions_projection(pos_enc)
@@ -3324,7 +3331,7 @@ def _build_network_inputs(self, inputs):
         if self.position_encoding_type == "trainable":
             pos_enc = self.position_embeddings(batch_size)
         elif self.position_encoding_type == "fourier":
-            pos_enc = self.position_embeddings(index_dims, batch_size, device=inputs.device)
+            pos_enc = self.position_embeddings(index_dims, batch_size, device=inputs.device, dtype=inputs.dtype)
 
         # Optionally project them to a target dimension.
         pos_enc = self.positions_projection(pos_enc)